AudioPaLM: A Large Language Model That Can Speak and Listen
Abstract
We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt. We release examples of our method at https://google-research.github.io/seanet/audiopalm/examples
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
AudioPaLM represents a significant advancement in multimodal language models, effectively bridging the gap between text and speech processing. The paper's comprehensive methodology and strong experimental results position it as a noteworthy contribution to the field of machine learning, particularly in audio and language processing.
Methodology
The methodology of AudioPaLM is robust, integrating both text and audio modalities into a unified framework. The use of a joint vocabulary for speech and text tokens allows for seamless task interleaving and leverages pre-trained text-based models for enhanced speech processing. The architecture is well-justified, with clear explanations of tokenization, model initialization, and training tasks. The approach to combining multiple tasks into a single model is innovative and demonstrates a thoughtful consideration of model efficiency and performance.
Results & evaluation
The experiments are comprehensive, covering a range of tasks including ASR, AST, and S2ST. The results show significant improvements over existing baselines, particularly in zero-shot scenarios, which is a notable achievement. The use of both objective metrics (like BLEU and WER) and subjective evaluations (like MOS) provides a well-rounded assessment of model performance. However, the paper could benefit from clearer presentation of results and more detailed comparisons with state-of-the-art methods.
Reproducibility
The paper provides sufficient details on the architecture, training data, and evaluation metrics, which supports reproducibility. However, the lack of a publicly available codebase or detailed implementation instructions may hinder full reproducibility. The authors mention using specific datasets and training setups, but sharing the code would greatly enhance the ability for others to replicate the findings.
Limitations
One limitation is the reliance on large pre-trained models, which may not be feasible for all research settings. Additionally, while the model shows strong performance in multilingual settings, the generalization to low-resource languages or dialects remains to be fully explored. The subjective evaluations, while valuable, could be influenced by the raters' biases and the quality of the datasets used.
Broader impact
The potential applications of AudioPaLM are significant, ranging from real-time translation services to enhancing accessibility for the hearing impaired. The integration of speech and text processing in a single model could lead to advancements in conversational AI and human-computer interaction. The model's ability to perform zero-shot translation is particularly impactful, suggesting future possibilities for multilingual communication without extensive training data. AudioPaLM represents a significant advancement in multimodal language models, effectively bridging the gap between text and speech processing. The paper's comprehensive methodology and strong experimental results position it as a noteworthy contribution to the field of machine learning, particularly in audio and language processing.
Curator note
Rubenstein et al., Google; LLM extended with audio tokens; jointly models text and speech
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